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We have developed an artificial neural network (ANN) based combustion model and have integrated it into a fluid mechanics code (KIVA3V) to produce a new analysis tool (titled KIVA3V-ANN) that can yield accurate HCCI predictions at very low computational cost. The neural network predicts ignition delay as a function of operating parameters (temperature, pressure, equivalence ratio and residual gas fraction). KIVA3V-ANN keeps track of the time history of the ignition delay during the engine cycle to evaluate the ignition integral and predict ignition for each computational cell. After a cell ignites, chemistry becomes active, and a two-step chemical kinetic mechanism predicts composition and heat generation in the ignited cells. KIVA3V-ANN has been validated by comparison with isooctane HCCI experiments in two different engines.

We have developed a methodology for predicting combustion and emissions in a Homogeneous Charge Compression Ignition (HCCI) Engine. This methodology combines a detailed fluid mechanics code with a detailed chemical kinetics code. Instead of directly linking the two codes, which would require an extremely long computational time, the methodology consists of first running the fluid mechanics code to obtain temperature profiles as a function of time. These temperature profiles are then used as input to a multi-zone chemical kinetics code. The advantage of this procedure is that a small number of zones (10) is enough to obtain accurate results. This procedure achieves the benefits of linking the fluid mechanics and the chemical kinetics codes with a great reduction in the computational effort, to a level that can be handled with current computers.